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Accurate crop mapping is crucial for ensuring food security. Recently, many studies have developed diverse crop mapping models based on deep learning. However, these models generally rely on a large amount of labeled crop samples to investigate the intricate relationship between the crop types of the samples and the corresponding remote sensing features. Moreover, their efficacy is often compromised when applied to other areas owing to the disparities between source and target data. To address this issue, a new multi-modal deep adaptation crop classification network (MDACCN) was proposed in this study. Specifically, MDACCN synergistically exploits time series optical and SAR images using a middle fusion strategy to achieve good classification capacity. Additionally, local maximum mean discrepancy (LMMD) is embedded into the model to measure and decrease domain discrepancies between source and target domains. As a result, a well-trained model in a source domain can still maintain satisfactory accuracy when applied to a target domain. In the training process, MDACCN incorporates the labeled samples from a source domain and unlabeled samples from a target domain. When it comes to the inference process, only unlabeled samples of the target domain are required. To assess the validity of the proposed model, Arkansas State in the United States was chosen as the source domain, and Heilongjiang Province in China was selected as the target domain. Supervised deep learning and traditional machine learning models were chosen as comparison models. The results indicated that the MDACCN achieved inspiring performance in the target domain, surpassing other models with overall accuracy, Kappa, and a macro-averaged F1 score of 0.878, 0.810, and 0.746, respectively. In addition, the crop-type maps produced by the MDACCN exhibited greater consistency with the reference maps. Moreover, the integration of optical and SAR features exhibited a substantial improvement of the model in the target domain compared with using single-modal features. This study indicated the considerable potential of combining multi-modal remote sensing data and an unsupervised domain adaptive approach to provide reliable crop distribution information in areas where labeled samples are missing.

Land cover and land use change (LCLUC) is a significant contributor to the changes in biomass carbon emissions. The state of Arkansas in the U.S.A. has experienced LCLUC over last five decades. This study combined geographic information system (GIS), remote sensing, and spatiotemporal analysis to quantify changes in vegetation carbon storage resulting from LCLUC during 2001–2019. The result showed that there were fluctuating changes among all land cover land use types, while the significant transition occurred mainly between forest and grassland. From 2001 to 2011, there were ~1973.8 km2 forest gain, mostly contributed from grassland/shrubland (~1448.8 km2), followed by farmland (~489.5 km2). The ~ 3575.3 km2 of forest was mainly changed into grassland/shrubland (~3343.4 km2) and built-up land (114.0 km2), leading to a net loss of ~1601.5 km2 in forest during this 10-year period. Similarly, the changes of grassland/shrubland, farmland, and built-up land with forest resulted in ~493.1 km2 net gain in forest from 2011 to 2019. During the process, a total of ~1.3 million tC biomass carbon was lost over the past 18 years in Arkansas, which is largely because of forest loss. However, due to the regrowth of trees, Arkansas also witnessed carbon gain during some periods. The spatiotemporal change of carbon storage and its drivers revealed by this study provide an important scientific basis for sustainable land use planning in Arkansas.

Augmented Reality (AR) is an innovative tool in education, enhancing learning experiences across multiple domains. This literature review explores the application of AR in education, with a particular focus on geographical learning. The study begins by tracing the historical development of AR, distinguishing it from Virtual Reality (VR) and highlighting its advantages in an educational context. The integration of AR into learning environments has been shown to improve engagement, comprehension of abstract concepts, and collaboration among students. The use of AR in geographical education through interactive applications, such as GeoAR and AR Sandbox, improves the exploration of spatial relationships, topographic maps, and environmental changes. Studies demonstrate that AR enhances students’ ability to recall information and understand geographical processes more effectively than with traditional methods. Furthermore, AR Sandbox implementations, including Illuminating Clay, SandScape, and AR Sandbox, are analyzed and compared. The paper also discusses future developments in AR for geography education for AR Sandbox, such as the integration of a mobile application for extended learning and improving computing solutions through Raspberry Pi. These advancements aim to make AR systems more accessible and to increase the benefits to both students and professors.

Timely, accurate, and repeatable crop mapping is vital for food security. Rice is one of the important food crops. Efficient and timely rice mapping would provide critical support for rice yield and production prediction as well as food security. The development of remote sensing (RS) satellite monitoring technology provides an opportunity for agricultural modernization applications and has become an important method to extract rice. This paper evaluated how a semantic segmentation model U-net that used time series Landsat images and Cropland Data Layer (CDL) performed when applied to extractions of paddy rice in Arkansas. Classifiers were trained based on time series images from 2017–2019, then were transferred to corresponding images in 2020 to obtain resultant maps. The extraction outputs were compared to those produced by Random Forest (RF). The results showed that U-net outperformed RF in most scenarios. The best scenario was when the time resolution of the data composite was fourteen day. The band combination including red band, near-infrared band, and Swir-1 band showed notably better performance than the six widely used bands for extracting rice. This study found a relatively high overall accuracy of 0.92 for extracting rice with training samples including five years from 2015 to 2019. Finally, we generated dynamic maps of rice in 2020. Rice could be identified in the heading stage (two months before maturing) with an overall accuracy of 0.86 on July 23. Accuracy gradually increased with the date of the mapping date. On September 17, overall accuracy was 0.92. There was a significant linear relationship (slope = 0.9, r2 = 0.75) between the mapped areas on July 23 and those from the statistical reports. Dynamic mapping is not only essential to assist farms and governments for growth monitoring and production assessment in the growing season, but also to support mitigation and disaster response strategies in the different growth stages of rice.

Core Ideas Topographic variation influenced soil nutrient distribution in a silvopasture system. High‐resolution digital maps of soil nutrients were generated. Terrain attributes identified topographic functional units as management zones. Level of soil nutrients in topographic functional units were different. Topography plays a crucial role in spatial distribution of nutrients in soils; however, studies to quantify topographic influence on soil nutrient distribution from a silvopasture system are mostly lacking. To address this question, a 4.3‐ha silvopasture site in northwest Arkansas was selected and a total of 51 topsoil (0–15 cm thickness) samples were collected and analyzed for primary (total N [TN], P, K), secondary (Ca, Mg, S), and micronutrients (Fe, Zn, Cu, Mn, B, Na). Topographic information was acquired from 12 terrain attributes derived from a 1‐m digital elevation model. The prediction model was based on random forest. Results showed TN, S, and P were best predicted, whereas Cu, Ca, and Mn had the lowest prediction performance. Levels of S, Ca, Zn, Fe, and TN increased with SAGA wetness index, valley depth, flow accumulation, and multi‐resolution valley bottom flatness index. Normalized height and slope height were positively related to Na but negatively to B and Cu distribution. Aspect had a positive influence on P and Mg concentrations. Based on terrain attributes, the study site could be divided into four topographic functional units (TFU), namely A, B, C, and D; TFU A had the highest nutrients present, whereas TFU B had the lowest P, K, Zn, Cu, Fe, and Ca but highest Na content. However, Mn, Mg, and B did not vary among TFUs. This study affirmed topographic influences on soil nutrient distribution, and the resulting continuous soil nutrient maps are useful for fine‐tuning production systems through optimum nutrient and pasture management.

Effective exploration of spatially referenced educational achievement data can help educational researchers and policy analysts accelerate interpretation of datasets to gain valuable insights. This paper illustrates the use of Geographic Information Systems (GIS) to analyze educational achievement gaps in Arkansas. It introduces the Geographic Academic Policy Series (GAPS) and presents one example of GAPS as a case study using GIS in the education policy analysis. The Geographic Academic Policy Series, developed in the National Office for Research on Measurement and Evaluation Systems, provides a visual \"snapshot\" of achievement relative to important policy issues. The GAPS series displays maps in conjunction with state-wide summaries of educational statistics, but does not require complicated understanding of statistics or methodology by the user. Currently, the GAPS series examines the relationships between school district academic performance and other district level variables including percent of students participating in Free and Reduced Lunch Programs (a proxy measure for poverty), school district size, and per pupil spending. In addition, district performance on the ACT exam, which is completed by students intending to attend college, is presented and examined in relation to district size. The GAPS series also includes state maps identifying the academic performance status of all districts and schools in Arkansas relative to NCLB. Policy makers have been particularly interested in the GAPS series, noting that it provides them with an effective method for examining academic achievement statewide.

Two-hundred-fifty-three turtles, representing 26 species within 5 families (Chelydridae, Emydidae, Kinosternidae, Testudinidae, Trionychidae) were examined for coccidia. Of these, 127 (50%) were found to harbor 1 or more of 28 species of eimerians, or isosporan, or both. One-hundred-thirteen (89%) of the infected turtles were aquatic species, whereas only 14 (11%) of the infected turtles were terrestrial species. Two-fold more aquatic turtles were infected with coccidia (113 of 200, 57%) compared to only 26% (14 of 53) of the terrestrial species. This report documents 14 new host and 8 new geographic records for eimerians from turtles in Arkansas and Texas.